With the advent of automated systems for fire prevention and fire fighting, the need to improve fire detection systems by means of providing fast, accurate and reliable fire detection systems has increased. For example, the U.S. Navy program Damage Control-Automation for Reduced Manning (DC-ARM) is focused on enhancing automation of ship functions and damage control systems. A key element to this objective is to improve its current fire detection systems. As in many applications, it is desired to increase detection sensitivity, decrease the detection time and increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed such that the fire detection systems can provide quick remote and automatic fire suppression capability. The use of multi-criteria based detection technology continues to offer the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to nuisance alarm sources. One way to accomplish this is to develop an early warning system that can process the output from sensors that measure multiple signatures of a developing fire or from analyzing multiple aspects of a given sensor output (e.g., rate of rise as well as absolute value).
The microprocessor has led to an explosion of sensor technology available for fire detection. Sensors that detect levels of CO, CO2, H2, Hydrocarbons, HCL, HCN, H2S, SO2, NO2, temperature, humidity, etc. are useful in the detection of some of the chemical and physical signatures for various types of fires, as well as Photoelectric and Ionization smoke detectors. When coupled with a microprocessor, these sensors produce digital output that can be quantified and processed as raw data. This sensor technology is readily available.
One or more of these sensors can be combined in a system to create an array, or sensor package with will monitor and detects various characteristic signatures for a fire and provide a block of data that can be processed to determine if a fire exists. However, often some of the various parameters used to detect fires overlap with non-urgent conditions, such as burned toast, thus causing a system to issue a fire condition/alarm when one of an urgent nature does not exist. These are known generally as nuisance alarms, and often have the effect of reducing the efficiency of response to actual fires through misallocation of fire fighting resources or though general apathy by eroding confidence in the accuracy of the fire detection and alarm system.
One way to address this is through the accurate and efficient processing of the data provided by the sensor array. Thus there exist a need for a system and method to efficiently process data and quickly identify fire signatures from a multi-criteria fire detection sensor array.